Recent advances in metric learning rely heavily on supervised learning using large-scale datasets.
Manual annotation of such datasets is however costly, and thus could limit the class diversity of training data and in consequence the generalization capability of learned models.
Unsupervised metric learning has been studied to resolve this issue.
Existing methods in this line of research mainly synthesize class information of training data by assigning a surrogate class per training instance [1]}, [2]}, [3]}, [4]} or discovering pseudo classes through \(k\) -means clustering [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, hierarchical clustering [8]}, or random walk [12]}.
Although these methods have demonstrated impressive results without using groundtruth labels in training, they often fail to
capture intra-class variation [1]}, [2]}, [3]}, [4]} or impose substantial computational burden due to the off-the-shelf techniques [5]}, [6]}, [7]}, [8]}, [9]}, [12]}, [10]}.
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